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#testing very variable threshold and steepness

set.seed(123)

global.threshold<-0:100

global.steepness<-10:100

threshold.for.action<- 0.9

decay.rate<- 0.9

test.network.2<- net.barabasi.albert(100,2, detectCores(), FALSE) %>%
  add_influencer(influencer.degree=25, population=100)

draw.net.large.network(test.network.2, influencer=101)

test.2.connection.matrix<- initialize_matrix(matrix(,101,101), test.network.2, 101)

test.2.activation.matrix<- matrix(0,cycles,101) %>%
  activate_influencer(population=100) %>%
  update.rule(.,test.2.connection.matrix, population = 100)

test.2.data<- data.frame(test.2.activation.matrix) %>%
  gather(key="Node", value= "activation") %>%
  cbind.data.frame(cycle=rep(seq(1:100),101))

new.actions.each.cycle.plot(test.2.activation.matrix, 100)

plot.by.degree(test.2.data, test.2.connection.matrix, 100)

Ignoring unknown aesthetics: line

#testing uniform

set.seed(123)

global.threshold<-50:50

global.steepness<-10:10

threshold.for.action<- 0.9

decay.rate<- 0.9

influencer.degree<- 25

test.network.3<-net.barabasi.albert(100,2, detectCores(), FALSE) %>%
  add_influencer(influencer.degree=influencer.degree, population=100)

draw.net.large.network(test.network.3, influencer=101)

test.3.connection.matrix<- initialize_matrix(matrix(,101,101), test.network.3, 101)

test.3.activation.matrix<- matrix(0,cycles,101) %>%
  activate_influencer(population=100) %>%
  update.rule(.,test.3.connection.matrix, population = 100)

test.3.data<- data.frame(test.3.activation.matrix) %>%
  gather(key="Node", value= "activation") %>%
  cbind.data.frame(cycle=rep(seq(1:100),101))

new.actions.each.cycle.plot(test.3.activation.matrix, 100)

plot.by.degree(test.3.data, test.3.connection.matrix, 100)

Ignoring unknown aesthetics: line

testing very high vs. low influencer degree high degree

set.seed(123)

global.threshold<-50:50

global.steepness<-10:10

threshold.for.action<- 0.9

decay.rate<- 0.9

influencer.degree<- 50

test.network.4<-net.barabasi.albert(100,2, detectCores(), FALSE) %>%
  add_influencer(influencer.degree=influencer.degree, population=100)

draw.net.large.network(test.network.4, influencer=101)

test.4.connection.matrix<- initialize_matrix(matrix(,101,101), test.network.4, 101)

test.4.activation.matrix<- matrix(0,cycles,101) %>%
  activate_influencer(population=100) %>%
  update.rule(.,test.4.connection.matrix, population = 100)

test.4.data<- data.frame(test.4.activation.matrix) %>%
  gather(key="Node", value= "activation") %>%
  cbind.data.frame(cycle=rep(seq(1:100),101))

new.actions.each.cycle.plot(test.4.activation.matrix, 100)

plot.by.degree(test.4.data, test.4.connection.matrix, 100)

Ignoring unknown aesthetics: line

#low degree

set.seed(123)

global.threshold<-50:50

global.steepness<-10:10

threshold.for.action<- 0.9

decay.rate<- 0.9

influencer.degree<- 10

test.network.5<-net.barabasi.albert(100,2, detectCores(), FALSE) %>%
  add_influencer(influencer.degree=influencer.degree, population=100)

draw.net.large.network(test.network.5, influencer=101)

test.5.connection.matrix<- initialize_matrix(matrix(,101,101), test.network.5, 101)

test.5.activation.matrix<- matrix(0,cycles,101) %>%
  activate_influencer(population=100) %>%
  update.rule(.,test.5.connection.matrix, population = 100)

test.5.data<- data.frame(test.5.activation.matrix) %>%
  gather(key="Node", value= "activation") %>%
  cbind.data.frame(cycle=rep(seq(1:100),101))

new.actions.each.cycle.plot(test.5.activation.matrix, 100)

plot.by.degree(test.5.data, test.5.connection.matrix, 100)

Ignoring unknown aesthetics: line